Identification Problems in the Social Sciences

社会科学中的识别问题

• 批准号: 9223220
• 负责人: Charles Manski
• 金额: $ 20.42万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-07-01 至 1997-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9223220&HistoricalAwards=false
• 关键词: Identification; Problems; Social; Sciences

Econometricians have long found it useful to separate inferential problems into statistical and identification components. Studies of identification seek to characterize the conclusions that could be drawn if the researcher had available a sample of unlimited size. Studies of statistical inference seek to characterize the generally weaker conclusions that can be drawn given a sample of positive but finite size. Statistical and identification problems limit in distinct ways the conclusions that may be drawn in empirical research. Statistical problems are most severe when the available sample is small. Identification problems are most severe when the researcher knows little about the population under study and the sampling process yields only weak data on the population. The proposed research continues the investigator's program of work on identification problems in the social sciences. Four projects are planned: (1) continued research on the selection problem; (2) analysis of identification in the presence of errors in data; (3) study of identification of endogenous social effects; and (4) investigation of the use of subjective data to identify personal expectations. One of the most exciting and novel parts of the project involves the use of subjective data to identify personal expectations. Distinguishing the objective and subjective determinants of behavior may be the most challenging identification problem facing social scientists. It is certainly the problem that most clearly separates the social from the natural sciences, where the units of analysis are not thought of as possessing free will. For many years, economists have exercised a self-imposed prohibition on the use of subjective data in empirical analysis. Economists have sought to infer the subjective constructs of economic theory, namely preferences and expectations, almost entirely from data on actual choices. But, economists do not know whether they are making valid inferences on preferences or wrong inferences based on incorrect expectations assumptions. This project develops methods that will permit economists to rethink their research conventions and entertain seriously the collection and analysis of subjective data on expectations and preferences.

计量经济学家长期以来一直发现，将推理问题分为统计和识别两个部分是有用的。认同感研究试图描述如果研究人员有无限大小的样本可以得出的结论。统计推断的研究试图刻画通常较弱的结论，这些结论可以在给定一个肯定但有限大小的样本的情况下得出。统计和识别问题以不同的方式限制了在实证研究中可能得出的结论。当可用样本较小时，统计问题最为严重。当研究人员对所研究的人群知之甚少，并且抽样过程只产生关于该人群的薄弱数据时，识别问题最为严重。拟议的研究继续了研究人员在社会科学中识别问题的工作计划。计划了四个项目：(1)继续研究选择问题；(2)在数据存在错误的情况下进行识别分析；(3)内生社会影响识别研究；(4)使用主观数据识别个人期望的调查。该项目最令人兴奋和最新颖的部分之一涉及使用主观数据来确定个人期望。区分行为的客观和主观决定因素可能是社会科学家面临的最具挑战性的识别问题。这当然是最明显地将社会科学与自然科学区分开来的问题，在自然科学中，分析单位不被认为拥有自由意志。多年来，经济学家一直自我禁止在实证分析中使用主观数据。经济学家试图推断经济理论的主观结构，即偏好和预期，几乎完全从实际选择的数据中推断出来。但是，经济学家不知道他们是在对偏好做出有效的推断，还是基于不正确的预期假设做出了错误的推断。这个项目开发的方法将允许经济学家重新思考他们的研究惯例，并认真考虑收集和分析有关预期和偏好的主观数据。